Method and device for providing a sparse gaussian process model for calculation in an engine control unit

ABSTRACT

A method for determining a sparse Gaussian process model to be carried out in a solely hardware-based model calculation unit includes: providing supporting point data points, a parameter vector based thereon, and corresponding hyperparameters; determining or providing virtual supporting point data points for the sparse Gaussian process model; and determining a parameter vector Q y * for the sparse Gaussian process model with the aid of a Cholesky decomposition of a covariant matrix K M  between the virtual supporting point data points and as a function of the supporting point data points, the parameter vector based thereon, and the corresponding hyperparameters, which define the sparse Gaussian process model.

RELATED APPLICATION INFORMATION

The present application claims priority to and the benefit of Germanpatent application no. 10 2013 227 183.2, which was filed in Germany onDec. 27, 2013, the disclosure of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates in general to engine control units, inwhich function models are implemented as data-based function models. Inparticular, the present invention relates to methods for determining asparse Gaussian process model from provided supporting point data.

BACKGROUND INFORMATION

The use of data-based function models is provided for the implementationof function models in control units, in particular engine control unitsfor internal combustion engines. Parameter-free data-based functionmodels are frequently used, since they may be prepared without specificspecifications from training data, i.e., a set of training data points.

One example of a data-based function model is represented by theso-called Gaussian process model, which is based on the Gaussian processregression. The Gaussian process regression is a multifaceted method fordatabase modeling of complex physical systems. Regression analysis istypically based on large quantities of training data, so that it isadvantageous to use approximate approaches, which may be analyzed moreefficiently.

For the Gaussian process model, the possibility exists of a sparseGaussian process regression, during which only a representative set ofsupporting point data is used to prepare the data-based function model.For this purpose, the supporting point data must be selected or derivedin a suitable way from the training data.

The publications by E. Snelson et al., “Sparse Gaussian Processes usingPseudo-inputs”, 2006 Neural Information Processing Systems 18 (NIPS) andCsató, Lehel; Opper, Manfred, “Sparse On-Line Gaussian Processes”;Neural Computation 14: pages 641-668, 2002, discuss a method forascertaining supporting point data for a sparse Gaussian process model.

Other methods in this regard are discussed in Smola, A. J., Schölkopf,W., “Sparse Greedy Gaussian Process Regression”, Advances in NeuralInformation Processing Systems 13, pages 619-625, 2001, and Seeger, M.,Williams, C. K., Lawrence, N. D., “Fast-Forward Selection to Speed upSparse Gaussian Process Regression”, Proceedings of the 9thInternational Workshop on Artificial Intelligence and Statistics, 2003.

Furthermore, control modules having a main computing unit and a modelcalculation unit for calculating data-based function models in a controlunit are known from the related art. Thus, for example, the publicationDE 10 2010 028 259 A1 describes a control unit having an additionallogic circuit as a model calculation unit which is configured forcalculating exponential functions to assist in carrying out Bayesianregression methods, which are required in particular for calculatingGaussian process models.

The model calculation unit is configured as a whole for carrying outmathematical processes for calculating the data-based function modelbased on parameters and supporting points or training data. Inparticular, the functions of the model calculation unit are implementedsolely in hardware for efficient calculation of exponential andsummation functions, so that it is made possible to calculate Gaussianprocess models at a higher computing speed than may be carried out inthe software-controlled main computing unit.

SUMMARY OF THE INVENTION

According to the present invention, a method for determining a sparseGaussian process model according to the description herein, as well as amodel calculation unit, a control unit, and a computer program accordingto the further descriptions herein are provided.

Other advantageous embodiments are specified in the further descriptionherein.

According to a first aspect, a method is provided for determining asparse Gaussian process model to be carried out in a solelyhardware-based model calculation unit, including the following steps:

-   -   providing supporting point data points, a parameter vector based        thereon, and corresponding hyperparameters;    -   determining or providing virtual supporting point data points        for the sparse Gaussian process model; and    -   determining a parameter vector for Q_(y)* the sparse Gaussian        process model with the aid of a Cholesky decomposition of a        covariant matrix K_(M) between the virtual supporting point data        points and as a function of the supporting point data points,        the parameter vector based thereon, and the corresponding        hyperparameters, which define the sparse Gaussian process model.

The above-described method provides a possibility of preparing a sparseGaussian process model based on a number of predefined virtualsupporting point data points in a simple way.

Sparse Gaussian process models are substantially more memory-efficientthan conventional Gaussian process models, since only M<<N supportingpoint data points must be stored. One-fourth of the supporting pointdata points or less are frequently sufficient. Therefore, moredata-based function models may be stored in a physical model calculationunit. In addition, the analysis of the individual, smaller Gaussianprocess models may be carried out more rapidly.

Furthermore, the method may include the further following steps:

-   -   ascertaining a covariant matrix K_(N) between the conventional        supporting point data points, a covariant matrix K_(M) between        the virtual supporting point data points, and a covariant matrix        K_(MN) between the conventional and the virtual supporting point        data points;    -   determining a diagonal matrix A from K_(MN) ^(T)K_(M) ⁻¹K_(MN),        in particular using the Cholesky decomposition of the covariant        matrix K_(M) between the virtual supporting point data points;        and    -   determining a parameter vector Q_(y)*, based on the        hyperparameters for the sparse Gaussian process model based on        the diagonal matrix.

The method may include the further following steps:

-   -   determining an intermediate variable Q_(M)=K_(M)+K_(MN)(Λ+σ_(n)        ²I)⁻¹K_(MN) ^(T) from the diagonal matrix Λ while using a        Cholesky decomposition of the covariant matrix K_(MN) between        the conventional and the virtual supporting point data points;        and    -   determining a parameter vector Q_(y)* based on the        hyperparameters for the sparse Gaussian process model based on        the intermediate variable Q_(M).

It may be provided that parameter vector Q_(y)* for the sparse Gaussianprocess model is ascertained as Q_(y)*=L_(m) ^(−T)L_(m)⁻¹+K_(MN)(Λ+σ_(n) ²I)⁻¹Y, L_(M) corresponding to the Choleskydecomposition of intermediate variable Q_(M).

In particular, a jitter may be applied to hyperparameter vector Q_(y)*for the sparse Gaussian process model.

According to another aspect, a model calculation unit for carrying out acalculation of a sparse Gaussian process model is provided, the sparseGaussian process model being calculated based on the hyperparametersascertained according to the above method for the sparse Gaussianprocess model, derived parameter vector Q_(y)*, and the virtualsupporting point data points.

Specific embodiments will be explained in greater detail hereafter onthe basis of the appended drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic view of an overall system for ascertaining asparse Gaussian process model and the engine control unit on which thesparse Gaussian process model is implemented.

FIG. 2 shows a flow chart to illustrate a method for determining asparse Gaussian process model.

DETAILED DESCRIPTION

FIG. 1 shows an arrangement 1 having a modeling system 2, which iscapable of ascertaining a data-based function model, in particular aGaussian process model, based on training data recorded, for example, ina test stand (not shown). The training data provide training data pointsof one or multiple input variable(s) and one or multiple outputvariable(s), which describe a behavior of a physical system 3, forexample, an internal combustion engine.

The use of nonparametric, data-based function models is based on aBayesian regression method. The fundamentals of Bayesian regression aredescribed, for example, in C. E. Rasmussen et al., “Gaussian Processesfor Machine Learning,” MIT Press 2006. Bayesian regression is adata-based method which is based on a model. To prepare the model,measuring points of training data and associated output data of anoutput variable to be modeled are required. The preparation of the modelis carried out based on the use of supporting point data, which entirelyor partially correspond to the training data or are generated therefrom.Furthermore, abstract hyperparameters are determined, which parameterizethe space of the model functions and effectively weight the influence ofthe individual measuring points of the training data on the later modelprediction.

The abstract hyperparameters are determined by an optimization method.One possibility for such an optimization method is an optimization of amarginal likelihood p(Y|H, X). Marginal likelihood p(Y|H, X) describesthe plausibility of model parameters H, given the measured y values ofthe training data, represented as vector Y and the x values of thetraining data, represented as matrix X. In model training, p(Y|H, X) ismaximized by searching for suitable hyperparameters which result in acurve of the model function determined by the hyperparameters and thetraining data and which image the training data as precisely aspossible. To simplify the calculation, the logarithm of p(Y|H, X) ismaximized, since the logarithm does not change the consistency of theplausibility function.

The calculation of the Gaussian process model takes place according tothe calculation specification below. Input values {tilde over (x)}_(d)for a test point x (input variable vector) are first scaled andcentered, specifically according to the following formula:

$x_{d} = {\frac{- {\left( m_{x} \right)d}}{\left( s_{x} \right)d}.}$

In this formula, m_(x) corresponds to the mean value function withrespect to a mean value of the input values of the supporting pointdata, s_(x) corresponds to the variance of the input values of thesupporting point data, and d corresponds to the index for dimension D oftest point x.

The following equation is obtained as the result of the preparation ofthe nonparametric, data-based function model:

$v = {\sum\limits_{i = 1}^{N}{\left( Q_{y} \right)_{i}\sigma_{f}{{\exp \left( {{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{\left( {X_{i,d} - x_{d}} \right)^{2}}{l_{d}}}} \right)}.}}}$

Model value v thus ascertained is scaled with the aid of an outputscaling, specifically according to the following formula:

{tilde over (v)}=vs _(y) +m _(y).

In this formula, v corresponds to a scaled model value (output value) ata scaled test point x (input variable vector of dimension D), {tildeover (v)} corresponds to a (non-scaled) model value (output value) at a(non-scaled) test point ũ (input variable vector of dimension D), x_(i)corresponds to a supporting point of the supporting point data, Ncorresponds to the number of the supporting points of the supportingpoint data, D corresponds to the dimension of the input data/trainingdata/supporting point data space, and I_(d) and σ_(f) correspond to thehyperparameters from the model training, namely the length scale and theamplitude factor. Vector Q_(y) is a variable calculated from thehyperparameters and the training data. Furthermore, m_(y) corresponds tothe mean value function with respect to a mean value of the outputvalues of the supporting point data and s_(y) corresponds to thevariance of the output values of the supporting point data.

Modeling system 2 furthermore carries out a method for processing theascertained or provided training data, to provide the data-basedfunction model with the aid of hyperparameters and supporting pointdata, which represent a subset of the training data. In this way, aso-called sparse Gaussian process model is prepared.

These supporting point data and hyperparameters are transferred into acontrol unit 4 and stored therein. Control unit 4 is connected to aphysical system 3, for example, an internal combustion engine, which isoperated with the aid of the data-based function model.

FIG. 1 furthermore shows a schematic view of a hardware architecture foran integrated control module, for example, in the form of amicrocontroller, in which a main computing unit 42 and a modelcalculation unit 43 are provided in an integrated way for the solelyhardware-based calculation of a data-based function model. Thehyperparameters and supporting point data are stored in a storage unit41. Main computing unit 42, storage unit 41, and model calculation unit43 have a communication link to one another via an internalcommunication link 44, for example, a system bus.

Main computing unit 42, which is provided as a microcontroller, isconfigured to calculate function values of the provided data-basedfunction model with the aid of a software-determined algorithm. Toaccelerate the calculation and to relieve microcontroller 42, it isprovided that model calculation unit 43 is used. Model calculation unit43 is completely implemented in hardware and is capable only of carryingout a certain calculation specification, which is essentially based onrepeated calculations of an addition function, a multiplicationfunction, and an exponential function. Fundamentally, model calculationunit 43 is thus essentially hardwired and is accordingly not configuredto execute a software code, as in the case of main computing unit 42.

Alternatively, an approach is possible in which model calculation unit43 provides a restricted, highly specialized command set for calculatingthe data-based function model. However, a processor is not provided inmodel calculation unit 43 in any specific embodiment. This enablesresource-optimized implementation of such a model calculation unit 43 oran area-optimized setting in an integrated construction.

In such a control unit 4, in addition to conventional Gaussian processmodels, sparse Gaussian process models may also be calculated. Since, inthe case of sparse Gaussian process models, the quantity of supportingpoint data is significantly less than in conventional Gaussian processmodels, the storage capacity to be provided of storage unit 41 forstoring the supporting point data may be reduced or multiple data setsof training data of multiple sparse Gaussian process models may bestored in storage unit 41.

A conventional Gaussian process regression uses the given supportingpoint data points/training data points for calculating the covariantmatrix. The model prediction is obtained in the form

${y = {{k_{x}^{T} \cdot Q_{y}} = {\sum\limits_{i = 1}^{N}{\sigma_{f} \cdot \left( Q_{y} \right)_{i} \cdot ^{{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{{({x_{d} - x_{i,d}})}^{2}}{l_{d}}}}}}}},$

where k_(x) ^(T), Q_(y)εR^(N) applies. It is to be emphasized that k_(x)^(T) represents the covariant vector between query point x and thesupporting point data points. This is calculated by the “squaredexponential” core as

$\left( k_{x} \right)_{i} = {{K\left( {x,x_{i}} \right)} = {\sigma_{f}{{\exp \left( {{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{\left( {x_{d} - x_{i,d}} \right)^{2}}{l_{d}}}} \right)}.}}}$

In the case of sparse Gaussian process models, the essential idea is toreplace the given supporting point data, which are formed by the “real”supporting point data points, with “virtual”, i.e., artificiallygenerated supporting point data points. M artificial points aregenerated and suitably positioned by an optimizer in such a way that themodel prediction of a sparse Gaussian process model using the virtualsupporting point data points corresponds as exactly as possible to thatof the Gaussian process model using the original supporting point datapoints. By integrating out the artificial y data, it is only necessaryto optimize M virtual X positions x _(i).

The model prediction for the sparse Gaussian process model results as

y=k _(*) ^(T) Q _(M) ⁻¹ K _(MN)(Λ−σ_(n) ² I)⁻¹ Y,

where k_(*) ^(T)εR^(M), Q_(M)εR^(M×M), K_(MN)εR^(M×N), Λ is anN-dimensional diagonal matrix, and Y is the vector of the y values ofthe original supporting point data points.

In the formula, k_(*) ^(T) is again the covariant vector, but calculatedthis time between query point x and the M-dimensional vector of virtualsupporting point data points The vector multiplied therein as a scalarproduct is provided, however, by the expression

Q _(y) *=Q _(M) ⁻¹ K _(MN)(Λ−σ_(n) ² I)⁻¹ Y

The same form as for the prediction of conventional Gaussian processesis thus obtained:

${y = {{k_{*}^{T}Q_{y}^{*}} = {\sum\limits_{i = 1}^{M}{\sigma_{f} \cdot \left( Q_{y}^{*} \right)_{i} \cdot ^{{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{{({x_{d} - {\overset{\_}{x}}_{i,d}})}^{2}}{l_{d}}}}}}}},$

if suitable values are used for parameter vector and the virtualsupporting point data points.

FIG. 2 schematically shows a flow chart to illustrate a method forproviding a sparse Gaussian process model using hyperparameters andsupporting point data.

The essential step in the preparation of the sparse Gaussian processmodel in the form of the algorithm available on model calculation unit43 is the calculation of vector Q_(y)*. Multiple possibilities exist forthis purpose; before they are described, however, some notation mustfirstly be introduced.

variable meaning N number of the supporting point data points in theoriginal Gaussian process model M number of the virtual supporting pointdata points x _(i) ∈ R^(D) i-th virtual supporting point data point Yvector of the y values of the supporting point data points. (N elements)K(x_(p), x_(q)) covariant function (squared exponential) (K_(N))_(i, j)= K(x_(i), x_(j)) covariant matrix of the supporting point data points(K_(M))_(i, j) = K( x _(i), x _(j)) covariant matrix of the virtualsupporting point data points (K_(NM))_(i, j) = K(x_(i), x _(j))covariant matrix between real and virtual supporting point data points(is also used transposed as K_(MN)) (k_(i))_(j) = (K_(NM))_(i, j) k_(i)is the i-th line of matrix K_(NM) λ_(i) = (K_(N))_(i, j) − k_(i) ^(T)K_(M) ⁻¹ k_(i) intermediate value λ ∈ R^(N) Λ = diag(λ) diagonal matrixwith λ on the diagonal Q_(M) = K_(M) + K_(MN) (Λ − σ_(n) ²I)⁻¹ K_(NM)intermediate variable (k_(*))_(i) = K( x _(i), x*) covariant of virtualsupport point data point x _(i) with query point x*

In addition, the Cholesky method for solving equation systems having apositive defined square matrix is also used.

For a positive defined square matrix K, a Cholesky decomposition L mayalways be calculated, so that L is an upper triangular matrix with theproperty

L ^(T) L=K.

To solve the equation system K·x=v, the expression K⁻¹v must becalculated. This is carried out with the aid of the Choleskydecomposition as follows:

K ⁻¹ v=(L ^(T) L)⁻¹ v=L ⁻¹ L ^(−T) v.

In the formula, L^(−T)=(L⁻¹)^(T) denotes the transposed inverse. Since Lis an upper triangular matrix, the expression may be calculated by aforward substitution and a reverse substitution.

Expressions of the form v^(T)K⁻¹v for a positive defined matrix K and avector v may be represented with the aid of the Cholesky decompositionas follows:

v ^(T) K ⁻¹ v=vT(LL ^(T))⁻¹ v=(L ⁻¹ v)^(T)(L ⁻¹ v)=∥L ⁻¹ v∥ ₂ ².

In conjunction with Gaussian processes, K is typically a covariantmatrix and therefore square and positively-semi-definite. For thepositive-definite case, the above equations may thus be used. If thematrix is positive-semi-definite, a jitter (for example, a value of10⁻⁶) is thus typically added to the diagonal of matrix K, to obtain apositive-definite matrix.

Two methods for determining vector Q_(y)* will be explained hereafter.

1) Direct Method

The direct conversion of Q_(y)*=Q_(M) ⁻¹+K_(MN)(Λ−σ_(n) ²I)⁻¹Y is onepossible procedure. If possible, the Cholesky decomposition is used toavoid direct calculations of inverse matrices. The calculation of Q_(y)*is carried out according to the following steps, which will be explainedin conjunction with FIG. 2:

In step S1, matrices K_(M), K_(N), and K_(MN) are calculated.

Subsequently, in step S2, Λ=diag(K_(MN) ^(T)K_(M) ⁻¹K_(MN)) isdetermined using the Cholesky decomposition of K_(M) (with a jitter).

In step S3, (Λ+σ_(n) ²I)⁼¹ is calculated, Λ+σ_(n) ²I corresponding to adiagonal matrix which may simply be inverted element by element.

In step S4, Q_(M) is determined.

In step S5, the Cholesky decomposition L_(M)=chol(Q_(M)) of Q_(M) iscalculated. In this case, as in step S2, a jitter is added to Q_(M).This corresponds to the procedure as if matrix K_(M) were provided witha jitter and then used for calculating Q_(M).

Q_(y)*=L_(m) ^(−T)L_(m) ⁻¹+K_(MN)(Λ+σ_(n) ²I)⁻¹Y then results, a forwardor reverse substitution being necessary in each case. Y are the y valuesof the original training data, i.e., the same y values as are used forthe normal training of the Gaussian process model. (The reduction of thedimension takes place with the multiplication of K_(MN) from the left).

2) Matrix Factorization

A second procedure includes the use of a matrix factorization.

Firstly, new variables are introduced:

L=chol(K_(M))^(T)

V₁=L⁻¹K_(MN)

V₂=V_(j)σ_(n)√Λ+σ_(n) ²I⁻¹

y₂=σn√Λ+σ_(n) ²I⁻¹Y

L_(m)=chol(σ_(n) ²I+V₂V₂ ^(T))^(T)

l_(st)=L⁻¹k_(*)

l_(mst)=L_(m) ⁻¹l_(st)=L_(m) ⁻¹L⁻¹k_(*)

β=L_(m) ⁻¹(V₂y₂)

Since Λ is a diagonal matrix, √{square root over (Λ+σ_(n) ²I)} is theCholesky decomposition of Λ+σ_(n) ²I.

Matrix Q_(M) may be represented as

$\begin{matrix}{Q_{M} = {K_{M} + {{{\overset{\_}{K}}_{MN}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}K_{MN}^{T}}}} \\{= {K_{M} + {{LL}^{- 1}{K_{MN}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}\left( {L^{- 1}K_{MN}} \right)^{T}L^{T}}}} \\{= {{LL}^{T} + {{{LV}_{1}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}V_{1}^{T}L^{T}}}} \\{= {{L\left( {I + {{V_{1}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}V_{1}^{T}}} \right)}L^{T}}}\end{matrix}$

Therefore, Q_(M) ⁻¹ results as

Q _(M) ⁻¹=L ^(−T) (I+V ₁( Λ+σ_(n) ² I)⁻¹ V ₁ ^(T))⁻¹ L ⁻¹.  Formula 1

Under the consideration that Λ+σ_(n) ²I is a diagonal matrix, it followsthat

$\begin{matrix}{{V_{2}V_{2}^{T}} = {V_{1}\sigma_{n}{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}\left( {V_{1}\sigma_{n}{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}} \right)^{T}}} \\{= {V_{1}\sigma_{n}{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}\left( {{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}\sigma_{n}V_{1}^{T}} \right)}} \\{= {\sigma_{n}^{2}{V_{1}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}V_{1}^{T}}}\end{matrix}\mspace{14mu} {and}$

with formula 1, it results that

Q _(M) ⁻¹=σ_(n) ² L ^(−T)(σ_(n) ² I+V ₂ V ₂ ^(T))⁻¹ L ⁻¹=σ_(n) ² L^(−T)(L _(m) L _(m) ^(T))⁻¹ L ⁻¹.  Formula 2

For further observation, the expression V₂y₂ must still be considered.In the rearrangement, the fact is again utilized that Λ+σ_(n) ²I is adiagonal matrix:

$\begin{matrix}\begin{matrix}{{V_{2}y_{2}} = {V_{1}\sigma_{n}{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}\sigma_{n}{\sqrt{\Lambda + {\sigma_{n}^{2}I}}}^{- 1}Y}} \\{= {\sigma_{n}^{2}{V_{1}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}Y}} \\{= {L^{- 1}\sigma_{n}^{2}{K_{MN}\left( {\Lambda + {\sigma_{n}^{2}I}} \right)}^{- 1}Y}}\end{matrix} & {{Formula}\mspace{14mu} 3}\end{matrix}$

The model prediction then results as

y=k _(*) ^(T) Q _(M) ⁻¹ K _(MN)(Λ+σ_(n) ² I)⁻¹ Y

By inserting formula 2, the following formula results

=k ₊ ^(T) L ^(−T)(L _(m) L _(m) ^(T))⁻¹ L ⁻¹σ_(n) ² K _(MN)(Λ+σ_(n) ²I)⁻¹ Y

By inserting formula 3, the following formulas result

$\begin{matrix}{= {k_{*}^{T}L^{- T}L_{m}^{- T}L_{m}^{- 1}V_{2}y_{2}}} & {{Formula}\mspace{14mu} 4} \\{= {\left( {L^{- 1}k_{*}} \right)^{T}L_{m}^{- T}\beta}} & \; \\{= {\left( {L_{m}^{- 1}L^{- 1}k_{*}} \right)^{T}\beta}} & \; \\{= {l_{mst}^{T}\beta}} & \; \\{= \left( {\beta^{T}l_{mst}} \right)^{T}} & {{Formula}\mspace{14mu} 5}\end{matrix}$

In the model analysis, expression l_(mst) may be determined. β^(T) iscalculated beforehand off-line and stored. To determine l_(mst), twoforward substitutions are to be calculated, which is relativelytime-consuming and therefore not possible on model calculation unit 43.

The only possibility for calculating this form of the model analysisusing the process provided on model calculation unit 43 is according toFormula 4. With the proviso

Q _(y) *=L ^(−T) L _(m) ^(−T) L _(m) ⁻¹ V ₂ y ₂.

the model prediction may be carried out according to the formula

$y = {{k_{*}^{T}Q_{y}^{*}} = {\sum\limits_{i = 1}^{M}{\sigma_{f} \cdot \left( Q_{y}^{*} \right)_{i} \cdot ^{{- \frac{1}{2}}{\sum\limits_{d = 1}^{D}\frac{{({x_{d} - {\overset{\_}{x}}_{i,d}})}^{2}}{l_{d}}}}}}}$

which is implemented on model calculation unit 43.

What is claimed is:
 1. A method for determining a sparse Gaussianprocess model, which is performed in a hardware-based model calculationunit, the method comprising: providing supporting point data points, aparameter vector based thereon, and corresponding hyperparameters;determining or providing virtual supporting point data points for thesparse Gaussian process model; and determining a parameter vector Q_(y)*for the sparse Gaussian process model with the aid of a Choleskydecomposition of a covariant matrix K_(M) between the virtual supportingpoint data points and as a function of the supporting point data points,the parameter vector based thereon, and the correspondinghyperparameters, which define the sparse Gaussian process model.
 2. Themethod of claim 1, further comprising: ascertaining a covariant matrixK_(N) between the conventional supporting point data points, a covariantmatrix K_(M) between the virtual supporting point data points, and acovariant matrix K_(MN) between the conventional and the virtualsupporting point data points; determining a diagonal matrix Λ fromK_(MN) ^(T) K_(M) ⁻¹K_(MN), using the Cholesky decomposition of thecovariant matrix K_(M) between the virtual supporting point data points;and determining a parameter vector Q_(y)* based on the hyperparametersfor the sparse Gaussian process model based on the diagonal matrix. 3.The method of claim 2, further comprising: determining an intermediatevariable Q_(M)=K_(M)+K_(MN)(Λ+σ_(n) ²I)⁻¹K_(MN) ^(T) from the diagonalmatrix Λ while using a Cholesky decomposition of the covariant matrixK_(MN) between the conventional and the virtual supporting point datapoints; and determining a parameter vector Q_(y)* based on thehyperparameters for the sparse Gaussian process model based on theintermediate variable Q_(M).
 4. The method of claim 1, wherein thevector Q_(y)* for the sparse Gaussian process model is ascertained asQ_(y)*=L_(m) ^(−T)L_(m) ⁻¹+K_(MN) (Λ+σ_(n) ²I)⁻¹Y, L_(M) correspondingto the Cholesky decomposition of intermediate variable Q_(M).
 5. Themethod of claim 1, wherein a jitter is applied to the hyperparametervector Q_(M) for the sparse Gaussian process model.
 6. A modelcalculation unit for carrying out a calculation of a sparse Gaussianprocess model, comprising: a determining arrangement to determine asparse Gaussian process model, which is performed in a hardware-basedmodel calculation unit, by performing the following: providingsupporting point data points, a parameter vector based thereon, andcorresponding hyperparameters; determining or providing virtualsupporting point data points for the sparse Gaussian process model; anddetermining a parameter vector Q_(y)* for the sparse Gaussian processmodel with the aid of a Cholesky decomposition of a covariant matrixK_(M) between the virtual supporting point data points and as a functionof the supporting point data points, the parameter vector based thereon,and the corresponding hyperparameters, which define the sparse Gaussianprocess model; and a calculating arrangement to calculate the sparseGaussian process model based on the hyperparameter vector Q_(M) for thesparse Gaussian process model, and the virtual supporting point datapoints.
 7. A control unit, comprising: a software-based main computingunit; and a model calculation unit for carrying out a calculation of asparse Gaussian process model, including: a determining arrangement todetermine a sparse Gaussian process model, which is performed in ahardware-based model calculation unit, by performing the following:providing supporting point data points, a parameter vector basedthereon, and corresponding hyperparameters; determining or providingvirtual supporting point data points for the sparse Gaussian processmodel; and determining a parameter vector Q_(y)* for the sparse Gaussianprocess model with the aid of a Cholesky decomposition of a covariantmatrix K_(M) between the virtual supporting point data points and as afunction of the supporting point data points, the parameter vector basedthereon, and the corresponding hyperparameters, which define the sparseGaussian process model; and a calculating arrangement to calculate thesparse Gaussian process model based on the hyperparameter vector Q_(M)for the sparse Gaussian process model, and the virtual supporting pointdata points.
 8. A computer readable medium having a computer program,which is executable by a processor, comprising: a program codearrangement having program code for determining a sparse Gaussianprocess model, which is performed in a hardware-based model calculationunit, by performing the following: providing supporting point datapoints, a parameter vector based thereon, and correspondinghyperparameters; determining or providing virtual supporting point datapoints for the sparse Gaussian process model; and determining aparameter vector Q_(y)* for the sparse Gaussian process model with theaid of a Cholesky decomposition of a covariant matrix K_(M) between thevirtual supporting point data points and as a function of the supportingpoint data points, the parameter vector based thereon, and thecorresponding hyperparameters, which define the sparse Gaussian processmodel.
 9. The computer readable medium of claim 8, further comprising:ascertaining a covariant matrix K_(N) between the conventionalsupporting point data points, a covariant matrix K_(M) between thevirtual supporting point data points, and a covariant matrix K_(MN)between the conventional and the virtual supporting point data points;determining a diagonal matrix Λ from K_(MN) ^(T)K_(M) ⁻¹K_(MN), usingthe Cholesky decomposition of the covariant matrix K_(M) between thevirtual supporting point data points; and determining a parameter vectorQ_(y)* based on the hyperparameters for the sparse Gaussian processmodel based on the diagonal matrix.
 10. An electronic control unit fordetermining a sparse Gaussian process model, which is performed in ahardware-based model calculation unit, comprising: a providingarrangement to provide supporting point data points, a parameter vectorbased thereon, and corresponding hyperparameters; a determining orproviding arrangement to determine or provide virtual supporting pointdata points for the sparse Gaussian process model; and a determiningarrangement to determine a parameter vector Q_(y)* for the sparseGaussian process model with the aid of a Cholesky decomposition of acovariant matrix K_(M) between the virtual supporting point data pointsand as a function of the supporting point data points, the parametervector based thereon, and the corresponding hyperparameters, whichdefine the sparse Gaussian process model.